Merge branch 'main' into Comp-Files

2-farm/translations/.dummy.md

@@ -1,9 +0,0 @@
-# Dummy File
-
-This file acts as a placeholder for the `translations` folder. <br>
-**Please remove this file after adding the first translation**
-
-For the instructions, follow the directives in the [translations guide](https://github.com/microsoft/IoT-For-Beginners/blob/main/TRANSLATIONS.md) .
-
-## THANK YOU
-We truly appreciate your efforts!

2-farm/translations/README.bn.md

@@ -0,0 +1,20 @@
+# কৃষিকাজে IoT
+
+জনসংখ্যা যেমন বাড়ছে, তেমনি কৃষির চাহিদাও বাড়ছে। কৃষিজমির পরিমাণ অতোটা পরিবর্তন না হলেও, জলবায়ুর পরিবর্তন ঠিকই হচ্ছে - যা কৃষকদের আরও বেশি সমস্যার মুখে ফেলে দিচ্ছে, বিশেষত সেই ২বিলিয়ন [জীবিকা নির্বাহী কৃষক](https://wikipedia.org/wiki/Subsistence_agriculture) যাদের ফসল বেড়ে ওঠার উপর নির্ভর করেই তাদের পরিবারের অন্নসংস্থান হয়। কোন ধরণের ফসল উৎপাদন করা যাবে, কখন কাজ শুরু করা উচিত, ফলনের বৃদ্ধি, শারিরীক শ্রমের পরিমাণ হ্রাস এবং কীটপতঙ্গগুলি সনাক্ত ও তাদেরকে বিনাশ করার বিষয়ে কৃষকদের অনেকাংশে সাহায্য করতে পারে আইওটি ।
+
+এই ৬টি লেসনে আমরা শিখবো কীভাবে কৃষিকাজ উন্নত ও স্বয়ংক্রিয় করতে ইন্টারনেট অফ থিংস প্রয়োগ করা যায়।
+
+> 💁 এই লেসনগুলোতে আমরা ক্লাউড রিসোর্স ব্যবহার করবো। যদি এই অধ্যায়ের সমস্ত পাঠ সম্পূর্ণ করা সম্ভব নাও হয়, তবুও [Clean up your project](../clean-up.md) অংশটি অবশ্যই দেখে নিতে হবে।
+
+## বিষয়াবলী
+
+1. [আইওটি দ্বারা উদ্ভিদ বৃদ্ধির পূর্বাভাস](lessons/1-predict-plant-growth/README.md)
+1. [মাটির আর্দ্রতা সনাক্তকরণ](lessons/2-detect-soil-moisture/README.md)
+1. [স্বয়ংক্রিয়ভাবে গাছে সেচকার্য](lessons/3-automated-plant-watering/README.md)
+1. [উদ্ভিদকে ক্লাউড থেকে নিয়ন্ত্রণ](lessons/4-migrate-your-plant-to-the-cloud/README.md)
+1. [ক্লাউড থেকে এপ্লিকেশন নিয়ন্ত্রণ](lessons/5-migrate-application-to-the-cloud/README.md)
+1. [উদ্ভিদের নিরাপত্তা নিশ্চিতকরণ](lessons/6-keep-your-plant-secure/README.md)
+
+## ক্রেডিট
+
+ ♥️ প্রতিটি লেসনই ভালোবাসার সাথে তৈরী করেছেন [Jim Bennett](https://GitHub.com/JimBobBennett)

5-retail/README.md

@@ -8,7 +8,7 @@ IoT can help with this, using AI models running on IoT devices to count stock, u
 
 In these 2 lessons you'll learn how to train image-based AI models to count stock, and run these models on IoT devices.
 
-> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [Clean up your project](../clean-up.md).
+> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [clean up your project](../clean-up.md).
 
 ## Topics
 

5-retail/lessons/1-train-stock-detector/README.md

@@ -105,7 +105,7 @@ You can train an object detector using Custom Vision, in a similar way to how yo
 
     Call your project `stock-detector`.
 
-    When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use a n*Object Detection* project type, and the *Products on Shelves* domain.
+    When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use the *Object Detection* project type, and the *Products on Shelves* domain.
 
     ![The settings for the custom vision project with the name set to fruit-quality-detector, no description, the resource set to fruit-quality-detector-training, the project type set to classification, the classification types set to multi class and the domains set to food](../../../images/custom-vision-create-object-detector-project.png)
 
@@ -135,7 +135,7 @@ To train your model you will need a set of images containing the objects you wan
 
     ![Tagging some tomato paste](../../../images/object-detector-tag-tomato-paste.png)
 
-    > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objecs in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time.
+    > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objects in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time.
 
 1. Follow the [Train the detector section of the Build an object detector quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/get-started-build-detector?WT.mc_id=academic-17441-jabenn#train-the-detector) to train the object detector on your tagged images.
 

5-retail/lessons/2-check-stock-device/README.md

@@ -116,7 +116,7 @@ You can use bounding boxes combined with probabilities to evaluate how accurate
 
 ![Two bonding boxes overlapping a can of tomato paste](../../../images/overlap-object-detection.png)
 
-In the example above, one bounding box indicated a predicted can of tomato paste at 78.3%. A second bounding box is slightly smaller, and is inside the first bounding box with a probability of 64.3%. You code can check the bounding boxes, see they overlap completely, and ignore the lower probability as there is no way one can can be inside another.
+In the example above, one bounding box indicated a predicted can of tomato paste at 78.3%. A second bounding box is slightly smaller, and is inside the first bounding box with a probability of 64.3%. Your code can check the bounding boxes, see they overlap completely, and ignore the lower probability as there is no way one can can be inside another.
 
 ✅ Can you think of a situation where is it valid to detect one object inside another?
 
@@ -157,7 +157,7 @@ Follow the relevant guide below to count stock using the results from the object
 
 Can you detect incorrect stock? Train your model on multiple objects, then update your app to alert you if the wrong stock is detected.
 
-Maybe even take this further and detect stock side by side on the same shelf, and see if something has been put in the wrong place bu defining limits on the bounding boxes.
+Maybe even take this further and detect stock side by side on the same shelf, and see if something has been put in the wrong place by defining limits on the bounding boxes.
 
 ## Post-lecture quiz
 

5-retail/lessons/2-check-stock-device/single-board-computer-count-stock.md

@@ -80,7 +80,7 @@ In the image shown above, the bounding boxes have a small overlap. If this overl
 
 ### Task - count stock ignoring overlap
 
-1. The Pip package [Shapely](https://pypi.org/project/Shapely/) can be used to calculate the intersection. If you are using a Raspberry Pi, you will need to instal a library dependency first:
+1. The Pip package [Shapely](https://pypi.org/project/Shapely/) can be used to calculate the intersection. If you are using a Raspberry Pi, you will need to install a library dependency first:
 
     ```sh
     sudo apt install libgeos-dev
@@ -158,6 +158,6 @@ In the image shown above, the bounding boxes have a small overlap. If this overl
 
 1. Run the app with the camera pointing at some stock on a shelf. The output will indicate the number of bounding boxes without overlaps that exceed the threshold. Try adjusting the `overlap_threshold` value to see predictions being ignored.
 
-> 💁 You can find this code in the [code-count/pi](code-count/pi) or [code-count/virtual-device](code-count/virtual-device) folder.
+> 💁 You can find this code in the [code-count/pi](code-count/pi) or [code-count/virtual-iot-device](code-count/virtual-iot-device) folder.
 
 😀 Your stock counter program was a success!

5-retail/lessons/2-check-stock-device/single-board-computer-object-detector.md

@@ -69,6 +69,6 @@ The code you used to classify images is very similar to the code to detect objec
 
     ![4 cans of tomato paste on a shelf with predictions for the 4 detections of 35.8%, 33.5%, 25.7% and 16.6%](../../../images/custom-vision-stock-prediction.png)
 
-> 💁 You can find this code in the [code-detect/pi](code-detect/pi) or [code-detect/virtual-device](code-detect/virtual-device) folder.
+> 💁 You can find this code in the [code-detect/pi](code-detect/pi) or [code-detect/virtual-iot-device](code-detect/virtual-iot-device) folder.
 
 😀 Your stock counter program was a success!

6-consumer/README.md

@@ -1,12 +1,12 @@
 # Consumer IoT - build a smart voice assistant
 
-The fod has been grown, driven to a processing plant, sorted for quality, sold in the store and now it's time to cook! One of the core pieces of any kitchen is a timer. Initially these started as hour glasses - your food was cooked when all the sand trickled down into the bottom bulb. They then went clockwork, then electric.
+The food has been grown, driven to a processing plant, sorted for quality, sold in the store and now it's time to cook! One of the core pieces of any kitchen is a timer. Initially these started as hour glasses - your food was cooked when all the sand trickled down into the bottom bulb. They then went clockwork, then electric.
 
 The latest iterations are now part of our smart devices. In kitchens in homes all throughout the world you'll hear cooks shouting "Hey Siri - set a 10 minute timer", or "Alexa - cancel my bread timer". No longer do you have to walk back to the kitchen to check on a timer, you can do it from your phone, or a call out across the room.
 
 In these 4 lessons you'll learn how to build a smart timer, using AI to recognize your voice, understand what you are asking for, and reply with information about your timer. You'll also add support for multiple languages.
 
-> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [Clean up your project](../clean-up.md).
+> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [clean up your project](../clean-up.md).
 
 ## Topics
 

6-consumer/lessons/1-speech-recognition/README.md

@@ -16,7 +16,7 @@ This video gives an overview of the Azure speech service, a topic that will be c
 
 'Alexa, timer status'
 
-'Alexa set a 8 minute timer called steam broccoli'
+'Alexa, set a 8 minute timer called steam broccoli'
 
 Smart devices are becoming more and more pervasive. Not just as smart speakers like HomePods, Echos and Google Homes, but embedded in our phones, watches, and even light fittings and thermostats.